Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations844.338
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory345.8 MiB
Average record size in memory429.4 B

Variable types

Numeric13
DateTime3
Categorical8
Text1

Alerts

assortment is highly overall correlated with store_typeHigh correlation
competition_open_since_year is highly overall correlated with competition_time_monthHigh correlation
competition_time_month is highly overall correlated with competition_open_since_yearHigh correlation
month is highly overall correlated with week_of_yearHigh correlation
promo2 is highly overall correlated with promo2_since_year and 1 other fieldsHigh correlation
promo2_since_year is highly overall correlated with promo2 and 2 other fieldsHigh correlation
promo_time_week is highly overall correlated with promo2 and 1 other fieldsHigh correlation
store_type is highly overall correlated with assortmentHigh correlation
week_of_year is highly overall correlated with monthHigh correlation
year is highly overall correlated with promo2_since_yearHigh correlation
state_holiday is highly imbalanced (99.3%) Imbalance
week_of_year has 10126 (1.2%) zeros Zeros
competition_time_month has 268025 (31.7%) zeros Zeros
promo_time_week has 421646 (49.9%) zeros Zeros

Reproduction

Analysis started2025-08-14 14:47:41.302011
Analysis finished2025-08-14 14:49:07.829220
Duration1 minute and 26.53 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

store
Real number (ℝ)

Distinct1115
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean558.42137
Minimum1
Maximum1115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:08.061879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile56
Q1280
median558
Q3837
95-th percentile1060
Maximum1115
Range1114
Interquartile range (IQR)557

Descriptive statistics

Standard deviation321.73086
Coefficient of variation (CV)0.57614353
Kurtosis-1.1988364
Mean558.42137
Median Absolute Deviation (MAD)278
Skewness0.00042588538
Sum4.7149639 × 108
Variance103510.75
MonotonicityNot monotonic
2025-08-14T11:49:08.209986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
335 942
 
0.1%
85 942
 
0.1%
262 942
 
0.1%
682 942
 
0.1%
769 942
 
0.1%
733 942
 
0.1%
494 942
 
0.1%
1097 942
 
0.1%
562 942
 
0.1%
423 942
 
0.1%
Other values (1105) 834918
98.9%
ValueCountFrequency (%)
1 781
0.1%
2 784
0.1%
3 779
0.1%
4 784
0.1%
5 779
0.1%
6 780
0.1%
7 786
0.1%
8 784
0.1%
9 779
0.1%
10 784
0.1%
ValueCountFrequency (%)
1115 781
0.1%
1114 784
0.1%
1113 784
0.1%
1112 779
0.1%
1111 779
0.1%
1110 783
0.1%
1109 622
0.1%
1108 780
0.1%
1107 623
0.1%
1106 784
0.1%

day_of_week
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5203497
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:08.325271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7237124
Coefficient of variation (CV)0.48964237
Kurtosis-1.2593474
Mean3.5203497
Median Absolute Deviation (MAD)2
Skewness0.019309987
Sum2972365
Variance2.9711843
MonotonicityNot monotonic
2025-08-14T11:49:08.422334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 144052
17.1%
2 143955
17.0%
3 141922
16.8%
5 138633
16.4%
1 137557
16.3%
4 134626
15.9%
7 3593
 
0.4%
ValueCountFrequency (%)
1 137557
16.3%
2 143955
17.0%
3 141922
16.8%
4 134626
15.9%
5 138633
16.4%
6 144052
17.1%
7 3593
 
0.4%
ValueCountFrequency (%)
7 3593
 
0.4%
6 144052
17.1%
5 138633
16.4%
4 134626
15.9%
3 141922
16.8%
2 143955
17.0%
1 137557
16.3%

date
Date

Distinct942
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Minimum2013-01-01 00:00:00
Maximum2015-07-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-14T11:49:08.584311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:49:08.758822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sales
Real number (ℝ)

Distinct21733
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6955.9591
Minimum46
Maximum41551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:08.922383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile3174
Q14859
median6369
Q38360
95-th percentile12668
Maximum41551
Range41505
Interquartile range (IQR)3501

Descriptive statistics

Standard deviation3103.8155
Coefficient of variation (CV)0.44620957
Kurtosis4.8540266
Mean6955.9591
Median Absolute Deviation (MAD)1694
Skewness1.5949288
Sum5.8731806 × 109
Variance9633670.8
MonotonicityNot monotonic
2025-08-14T11:49:09.149433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5674 215
 
< 0.1%
5558 197
 
< 0.1%
5483 196
 
< 0.1%
6049 195
 
< 0.1%
6214 195
 
< 0.1%
5723 194
 
< 0.1%
5449 192
 
< 0.1%
5489 191
 
< 0.1%
5140 191
 
< 0.1%
5041 190
 
< 0.1%
Other values (21723) 842382
99.8%
ValueCountFrequency (%)
46 1
< 0.1%
124 1
< 0.1%
133 1
< 0.1%
286 1
< 0.1%
297 1
< 0.1%
316 1
< 0.1%
416 1
< 0.1%
506 1
< 0.1%
520 1
< 0.1%
530 1
< 0.1%
ValueCountFrequency (%)
41551 1
< 0.1%
38722 1
< 0.1%
38484 1
< 0.1%
38367 1
< 0.1%
38037 1
< 0.1%
38025 1
< 0.1%
37646 1
< 0.1%
37403 1
< 0.1%
37376 1
< 0.1%
37122 1
< 0.1%

promo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
0
467463 
1
376875 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844.338
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 467463
55.4%
1 376875
44.6%

Length

2025-08-14T11:49:09.326864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T11:49:09.440979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 467463
55.4%
1 376875
44.6%

Most occurring characters

ValueCountFrequency (%)
0 467463
55.4%
1 376875
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 467463
55.4%
1 376875
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 467463
55.4%
1 376875
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 467463
55.4%
1 376875
44.6%

state_holiday
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.2 MiB
regular_day
843428 
public_holiday
 
694
easter_holiday
 
145
christmas
 
71

Length

Max length14
Median length11
Mean length11.002813
Min length9

Characters and Unicode

Total characters9.290.093
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular_day
2nd rowregular_day
3rd rowregular_day
4th rowregular_day
5th rowregular_day

Common Values

ValueCountFrequency (%)
regular_day 843428
99.9%
public_holiday 694
 
0.1%
easter_holiday 145
 
< 0.1%
christmas 71
 
< 0.1%

Length

2025-08-14T11:49:09.536460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T11:49:09.615165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
regular_day 843428
99.9%
public_holiday 694
 
0.1%
easter_holiday 145
 
< 0.1%
christmas 71
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 1687911
18.2%
r 1687072
18.2%
l 844961
9.1%
y 844267
9.1%
d 844267
9.1%
_ 844267
9.1%
u 844122
9.1%
e 843718
9.1%
g 843428
9.1%
i 1604
 
< 0.1%
Other values (8) 4476
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9290093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1687911
18.2%
r 1687072
18.2%
l 844961
9.1%
y 844267
9.1%
d 844267
9.1%
_ 844267
9.1%
u 844122
9.1%
e 843718
9.1%
g 843428
9.1%
i 1604
 
< 0.1%
Other values (8) 4476
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9290093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1687911
18.2%
r 1687072
18.2%
l 844961
9.1%
y 844267
9.1%
d 844267
9.1%
_ 844267
9.1%
u 844122
9.1%
e 843718
9.1%
g 843428
9.1%
i 1604
 
< 0.1%
Other values (8) 4476
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9290093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1687911
18.2%
r 1687072
18.2%
l 844961
9.1%
y 844267
9.1%
d 844267
9.1%
_ 844267
9.1%
u 844122
9.1%
e 843718
9.1%
g 843428
9.1%
i 1604
 
< 0.1%
Other values (8) 4476
 
< 0.1%

school_holiday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
0
680893 
1
163445 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844.338
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 680893
80.6%
1 163445
 
19.4%

Length

2025-08-14T11:49:09.716257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T11:49:09.784196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 680893
80.6%
1 163445
 
19.4%

Most occurring characters

ValueCountFrequency (%)
0 680893
80.6%
1 163445
 
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 680893
80.6%
1 163445
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 680893
80.6%
1 163445
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 680893
80.6%
1 163445
 
19.4%

store_type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
a
457042 
d
258768 
c
112968 
b
 
15560

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844.338
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowc
2nd rowa
3rd rowa
4th rowc
5th rowa

Common Values

ValueCountFrequency (%)
a 457042
54.1%
d 258768
30.6%
c 112968
 
13.4%
b 15560
 
1.8%

Length

2025-08-14T11:49:09.871265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T11:49:09.947004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 457042
54.1%
d 258768
30.6%
c 112968
 
13.4%
b 15560
 
1.8%

Most occurring characters

ValueCountFrequency (%)
a 457042
54.1%
d 258768
30.6%
c 112968
 
13.4%
b 15560
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 457042
54.1%
d 258768
30.6%
c 112968
 
13.4%
b 15560
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 457042
54.1%
d 258768
30.6%
c 112968
 
13.4%
b 15560
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 457042
54.1%
d 258768
30.6%
c 112968
 
13.4%
b 15560
 
1.8%

assortment
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.5 MiB
basic
444875 
extended
391254 
extra
 
8209

Length

Max length8
Median length5
Mean length6.3901565
Min length5

Characters and Unicode

Total characters5.395.452
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic
2nd rowbasic
3rd rowbasic
4th rowextended
5th rowbasic

Common Values

ValueCountFrequency (%)
basic 444875
52.7%
extended 391254
46.3%
extra 8209
 
1.0%

Length

2025-08-14T11:49:10.062850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T11:49:10.157964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
basic 444875
52.7%
extended 391254
46.3%
extra 8209
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 1181971
21.9%
d 782508
14.5%
a 453084
 
8.4%
s 444875
 
8.2%
b 444875
 
8.2%
c 444875
 
8.2%
i 444875
 
8.2%
x 399463
 
7.4%
t 399463
 
7.4%
n 391254
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5395452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1181971
21.9%
d 782508
14.5%
a 453084
 
8.4%
s 444875
 
8.2%
b 444875
 
8.2%
c 444875
 
8.2%
i 444875
 
8.2%
x 399463
 
7.4%
t 399463
 
7.4%
n 391254
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5395452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1181971
21.9%
d 782508
14.5%
a 453084
 
8.4%
s 444875
 
8.2%
b 444875
 
8.2%
c 444875
 
8.2%
i 444875
 
8.2%
x 399463
 
7.4%
t 399463
 
7.4%
n 391254
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5395452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1181971
21.9%
d 782508
14.5%
a 453084
 
8.4%
s 444875
 
8.2%
b 444875
 
8.2%
c 444875
 
8.2%
i 444875
 
8.2%
x 399463
 
7.4%
t 399463
 
7.4%
n 391254
 
7.3%

competition_distance
Real number (ℝ)

Distinct655
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5961.8275
Minimum20
Maximum200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:10.360964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile130
Q1710
median2330
Q36910
95-th percentile20930
Maximum200000
Range199980
Interquartile range (IQR)6200

Descriptive statistics

Standard deviation12592.181
Coefficient of variation (CV)2.1121344
Kurtosis145.28866
Mean5961.8275
Median Absolute Deviation (MAD)1980
Skewness10.134908
Sum5.0337975 × 109
Variance1.5856303 × 108
MonotonicityNot monotonic
2025-08-14T11:49:10.576964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 9210
 
1.1%
50 6249
 
0.7%
350 6239
 
0.7%
1200 6069
 
0.7%
190 6066
 
0.7%
90 5607
 
0.7%
180 5421
 
0.6%
330 5294
 
0.6%
150 5292
 
0.6%
140 4684
 
0.6%
Other values (645) 784207
92.9%
ValueCountFrequency (%)
20 779
 
0.1%
30 3115
0.4%
40 3888
0.5%
50 6249
0.7%
60 2342
 
0.3%
70 3734
0.4%
80 2331
 
0.3%
90 5607
0.7%
100 3900
0.5%
110 4514
0.5%
ValueCountFrequency (%)
200000 2186
0.3%
75860 887
0.1%
58260 885
0.1%
48330 784
 
0.1%
46590 784
 
0.1%
45740 780
 
0.1%
44320 780
 
0.1%
40860 881
0.1%
40540 780
 
0.1%
38710 784
 
0.1%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7873553
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:10.732964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3099167
Coefficient of variation (CV)0.48765926
Kurtosis-1.2318753
Mean6.7873553
Median Absolute Deviation (MAD)3
Skewness-0.048451057
Sum5730822
Variance10.955548
MonotonicityNot monotonic
2025-08-14T11:49:10.839961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 112179
13.3%
4 98204
11.6%
11 86359
10.2%
3 80052
9.5%
7 76226
9.0%
12 63968
7.6%
6 63913
7.6%
10 63216
7.5%
5 58271
6.9%
2 56895
6.7%
Other values (2) 85055
10.1%
ValueCountFrequency (%)
1 37733
 
4.5%
2 56895
6.7%
3 80052
9.5%
4 98204
11.6%
5 58271
6.9%
6 63913
7.6%
7 76226
9.0%
8 47322
5.6%
9 112179
13.3%
10 63216
7.5%
ValueCountFrequency (%)
12 63968
7.6%
11 86359
10.2%
10 63216
7.5%
9 112179
13.3%
8 47322
5.6%
7 76226
9.0%
6 63913
7.6%
5 58271
6.9%
4 98204
11.6%
3 80052
9.5%

competition_open_since_year
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.3311
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:10.949808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile2002
Q12008
median2012
Q32014
95-th percentile2015
Maximum2015
Range115
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.5026278
Coefficient of variation (CV)0.0027371749
Kurtosis123.90308
Mean2010.3311
Median Absolute Deviation (MAD)2
Skewness-7.2173228
Sum1.6973989 × 109
Variance30.278913
MonotonicityNot monotonic
2025-08-14T11:49:11.064847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2013 170465
20.2%
2014 151774
18.0%
2015 91118
10.8%
2012 61716
 
7.3%
2005 46703
 
5.5%
2010 42715
 
5.1%
2011 41363
 
4.9%
2009 40711
 
4.8%
2008 40195
 
4.8%
2007 36125
 
4.3%
Other values (13) 121453
14.4%
ValueCountFrequency (%)
1900 622
 
0.1%
1961 779
 
0.1%
1990 3885
 
0.5%
1994 1552
 
0.2%
1995 1404
 
0.2%
1998 766
 
0.1%
1999 6213
 
0.7%
2000 7631
 
0.9%
2001 12157
1.4%
2002 20736
2.5%
ValueCountFrequency (%)
2015 91118
10.8%
2014 151774
18.0%
2013 170465
20.2%
2012 61716
 
7.3%
2011 41363
 
4.9%
2010 42715
 
5.1%
2009 40711
 
4.8%
2008 40195
 
4.8%
2007 36125
 
4.3%
2006 35543
 
4.2%

promo2
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
0
423292 
1
421046 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844.338
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 423292
50.1%
1 421046
49.9%

Length

2025-08-14T11:49:11.169789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T11:49:11.230415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 423292
50.1%
1 421046
49.9%

Most occurring characters

ValueCountFrequency (%)
0 423292
50.1%
1 421046
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 423292
50.1%
1 421046
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 423292
50.1%
1 421046
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 423292
50.1%
1 421046
49.9%

promo2_since_week
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.629083
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:11.322151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median22
Q337
95-th percentile47
Maximum52
Range51
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.288315
Coefficient of variation (CV)0.60469188
Kurtosis-1.1948145
Mean23.629083
Median Absolute Deviation (MAD)12
Skewness0.17039865
Sum19950933
Variance204.15594
MonotonicityNot monotonic
2025-08-14T11:49:11.455121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 69320
 
8.2%
40 56919
 
6.7%
31 42369
 
5.0%
10 42002
 
5.0%
5 39506
 
4.7%
1 34479
 
4.1%
13 33878
 
4.0%
37 33528
 
4.0%
22 32208
 
3.8%
18 30709
 
3.6%
Other values (42) 429420
50.9%
ValueCountFrequency (%)
1 34479
4.1%
2 9644
 
1.1%
3 9784
 
1.2%
4 9778
 
1.2%
5 39506
4.7%
6 10555
 
1.3%
7 9776
 
1.2%
8 9793
 
1.2%
9 20107
2.4%
10 42002
5.0%
ValueCountFrequency (%)
52 4342
 
0.5%
51 6424
 
0.8%
50 7188
 
0.9%
49 7030
 
0.8%
48 13442
1.6%
47 6307
 
0.7%
46 6408
 
0.8%
45 30480
3.6%
44 8061
 
1.0%
43 6428
 
0.8%

promo2_since_year
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.7979
Minimum2009
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:12.692752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12012
median2013
Q32014
95-th percentile2015
Maximum2015
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6601247
Coefficient of variation (CV)0.0008247846
Kurtosis-0.19791125
Mean2012.7979
Median Absolute Deviation (MAD)1
Skewness-0.78829569
Sum1.6994818 × 109
Variance2.7560141
MonotonicityNot monotonic
2025-08-14T11:49:12.767056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2013 257197
30.5%
2014 227630
27.0%
2015 103528
12.3%
2011 95035
 
11.3%
2012 60712
 
7.2%
2009 53824
 
6.4%
2010 46412
 
5.5%
ValueCountFrequency (%)
2009 53824
 
6.4%
2010 46412
 
5.5%
2011 95035
 
11.3%
2012 60712
 
7.2%
2013 257197
30.5%
2014 227630
27.0%
2015 103528
12.3%
ValueCountFrequency (%)
2015 103528
12.3%
2014 227630
27.0%
2013 257197
30.5%
2012 60712
 
7.2%
2011 95035
 
11.3%
2010 46412
 
5.5%
2009 53824
 
6.4%

is_promo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
0
713533 
1
130805 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844.338
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 713533
84.5%
1 130805
 
15.5%

Length

2025-08-14T11:49:12.859824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T11:49:12.919800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 713533
84.5%
1 130805
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 713533
84.5%
1 130805
 
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 713533
84.5%
1 130805
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 713533
84.5%
1 130805
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 844338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 713533
84.5%
1 130805
 
15.5%

year
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.6 MiB
2013
337924 
2014
310385 
2015
196029 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3.377.352
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2013 337924
40.0%
2014 310385
36.8%
2015 196029
23.2%

Length

2025-08-14T11:49:12.994954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T11:49:13.057952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2013 337924
40.0%
2014 310385
36.8%
2015 196029
23.2%

Most occurring characters

ValueCountFrequency (%)
2 844338
25.0%
0 844338
25.0%
1 844338
25.0%
3 337924
10.0%
4 310385
 
9.2%
5 196029
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3377352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 844338
25.0%
0 844338
25.0%
1 844338
25.0%
3 337924
10.0%
4 310385
 
9.2%
5 196029
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3377352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 844338
25.0%
0 844338
25.0%
1 844338
25.0%
3 337924
10.0%
4 310385
 
9.2%
5 196029
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3377352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 844338
25.0%
0 844338
25.0%
1 844338
25.0%
3 337924
10.0%
4 310385
 
9.2%
5 196029
 
5.8%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8457738
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:13.126506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3239595
Coefficient of variation (CV)0.56860898
Kurtosis-1.03319
Mean5.8457738
Median Absolute Deviation (MAD)3
Skewness0.25770643
Sum4935809
Variance11.048707
MonotonicityNot monotonic
2025-08-14T11:49:13.200512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 86335
10.2%
3 85975
10.2%
7 85576
10.1%
6 82571
9.8%
4 81726
9.7%
2 80239
9.5%
5 80099
9.5%
8 54411
6.4%
10 53291
6.3%
9 52321
6.2%
Other values (2) 101794
12.1%
ValueCountFrequency (%)
1 86335
10.2%
2 80239
9.5%
3 85975
10.2%
4 81726
9.7%
5 80099
9.5%
6 82571
9.8%
7 85576
10.1%
8 54411
6.4%
9 52321
6.2%
10 53291
6.3%
ValueCountFrequency (%)
12 50393
6.0%
11 51401
6.1%
10 53291
6.3%
9 52321
6.2%
8 54411
6.4%
7 85576
10.1%
6 82571
9.8%
5 80099
9.5%
4 81726
9.7%
3 85975
10.2%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.835706
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:13.283859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6833918
Coefficient of variation (CV)0.54834259
Kurtosis-1.1796706
Mean15.835706
Median Absolute Deviation (MAD)7
Skewness0.011112159
Sum13370688
Variance75.401292
MonotonicityNot monotonic
2025-08-14T11:49:13.385026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11 30119
 
3.6%
4 29471
 
3.5%
27 29270
 
3.5%
13 29261
 
3.5%
23 29239
 
3.5%
2 29233
 
3.5%
16 29202
 
3.5%
18 29058
 
3.4%
28 28365
 
3.4%
7 28357
 
3.4%
Other values (21) 552763
65.5%
ValueCountFrequency (%)
1 19366
2.3%
2 29233
3.5%
3 25056
3.0%
4 29471
3.5%
5 28172
3.3%
6 27566
3.3%
7 28357
3.4%
8 27959
3.3%
9 27067
3.2%
10 28156
3.3%
ValueCountFrequency (%)
31 15923
1.9%
30 26324
3.1%
29 23571
2.8%
28 28365
3.4%
27 29270
3.5%
26 26167
3.1%
25 27063
3.2%
24 28157
3.3%
23 29239
3.5%
22 27987
3.3%

week_of_year
Real number (ℝ)

High correlation  Zeros 

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.957035
Minimum0
Maximum52
Zeros10126
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-08-14T11:49:13.507097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median22
Q334
95-th percentile48
Maximum52
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.458681
Coefficient of variation (CV)0.62981482
Kurtosis-1.0233089
Mean22.957035
Median Absolute Deviation (MAD)12
Skewness0.26702406
Sum19383497
Variance209.05345
MonotonicityNot monotonic
2025-08-14T11:49:13.631733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 20119
 
2.4%
11 20098
 
2.4%
8 20093
 
2.4%
10 20079
 
2.4%
5 20066
 
2.4%
4 20063
 
2.4%
7 20053
 
2.4%
9 20051
 
2.4%
3 20044
 
2.4%
2 20040
 
2.4%
Other values (43) 643632
76.2%
ValueCountFrequency (%)
0 10126
1.2%
1 19448
2.3%
2 20040
2.4%
3 20044
2.4%
4 20063
2.4%
5 20066
2.4%
6 20039
2.4%
7 20053
2.4%
8 20093
2.4%
9 20051
2.4%
ValueCountFrequency (%)
52 5035
0.6%
51 8319
1.0%
50 12355
1.5%
49 12333
1.5%
48 12334
1.5%
47 12334
1.5%
46 12182
1.4%
45 12333
1.5%
44 12334
1.5%
43 11042
1.3%
Distinct137
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 MiB
2025-08-14T11:49:14.034796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5.910.366
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-30
2nd row2015-30
3rd row2015-30
4th row2015-30
5th row2015-30
ValueCountFrequency (%)
2015-20 6722
 
0.8%
2015-16 6722
 
0.8%
2013-42 6721
 
0.8%
2013-38 6721
 
0.8%
2013-41 6721
 
0.8%
2013-40 6721
 
0.8%
2015-15 6720
 
0.8%
2015-23 6720
 
0.8%
2015-26 6720
 
0.8%
2014-14 6718
 
0.8%
Other values (127) 777132
92.0%
2025-08-14T11:49:14.398850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1126233
19.1%
1 1123367
19.0%
2 1123094
19.0%
- 844338
14.3%
3 545102
9.2%
4 515558
8.7%
5 305579
 
5.2%
8 82840
 
1.4%
6 82795
 
1.4%
9 80848
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5910366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1126233
19.1%
1 1123367
19.0%
2 1123094
19.0%
- 844338
14.3%
3 545102
9.2%
4 515558
8.7%
5 305579
 
5.2%
8 82840
 
1.4%
6 82795
 
1.4%
9 80848
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5910366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1126233
19.1%
1 1123367
19.0%
2 1123094
19.0%
- 844338
14.3%
3 545102
9.2%
4 515558
8.7%
5 305579
 
5.2%
8 82840
 
1.4%
6 82795
 
1.4%
9 80848
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5910366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1126233
19.1%
1 1123367
19.0%
2 1123094
19.0%
- 844338
14.3%
3 545102
9.2%
4 515558
8.7%
5 305579
 
5.2%
8 82840
 
1.4%
6 82795
 
1.4%
9 80848
 
1.4%
Distinct173
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Minimum1900-01-01 00:00:00
Maximum2015-08-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-14T11:49:14.515660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:49:14.656923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

competition_time_month
Real number (ℝ)

High correlation  Zeros 

Distinct376
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.679672
Minimum-32
Maximum1407
Zeros268025
Zeros (%)31.7%
Negative70101
Negative (%)8.3%
Memory size12.9 MiB
2025-08-14T11:49:14.788730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-32
5-th percentile-7
Q10
median16
Q374
95-th percentile145
Maximum1407
Range1439
Interquartile range (IQR)74

Descriptive statistics

Standard deviation66.814412
Coefficient of variation (CV)1.6030455
Kurtosis126.85589
Mean41.679672
Median Absolute Deviation (MAD)17
Skewness7.3388556
Sum35191731
Variance4464.1657
MonotonicityNot monotonic
2025-08-14T11:49:14.914203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 268025
31.7%
1 9476
 
1.1%
7 5316
 
0.6%
5 5234
 
0.6%
4 5232
 
0.6%
6 5214
 
0.6%
9 5163
 
0.6%
8 5147
 
0.6%
10 5140
 
0.6%
11 5038
 
0.6%
Other values (366) 525353
62.2%
ValueCountFrequency (%)
-32 30
 
< 0.1%
-31 147
 
< 0.1%
-30 323
 
< 0.1%
-29 445
 
0.1%
-28 593
0.1%
-27 772
0.1%
-26 853
0.1%
-25 896
0.1%
-24 976
0.1%
-23 1139
0.1%
ValueCountFrequency (%)
1407 5
 
< 0.1%
1406 25
< 0.1%
1405 25
< 0.1%
1404 23
< 0.1%
1403 23
< 0.1%
1402 26
< 0.1%
1401 26
< 0.1%
1400 21
< 0.1%
1393 23
< 0.1%
1392 24
< 0.1%
Distinct167
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Minimum2009-07-27 00:00:00
Maximum2015-07-27 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-14T11:49:15.042180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:49:15.180034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

promo_time_week
Real number (ℝ)

High correlation  Zeros 

Distinct440
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.400699
Minimum-126
Maximum313
Zeros421646
Zeros (%)49.9%
Negative57241
Negative (%)6.8%
Memory size12.9 MiB
2025-08-14T11:49:15.320903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-126
5-th percentile-19
Q10
median0
Q3109
95-th percentile230
Maximum313
Range439
Interquartile range (IQR)109

Descriptive statistics

Standard deviation85.457559
Coefficient of variation (CV)1.5708908
Kurtosis0.1129961
Mean54.400699
Median Absolute Deviation (MAD)1
Skewness1.1033835
Sum45932577
Variance7302.9944
MonotonicityNot monotonic
2025-08-14T11:49:15.439283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 421646
49.9%
52 3910
 
0.5%
98 1872
 
0.2%
102 1847
 
0.2%
97 1830
 
0.2%
103 1828
 
0.2%
101 1778
 
0.2%
99 1777
 
0.2%
94 1770
 
0.2%
93 1764
 
0.2%
Other values (430) 404316
47.9%
ValueCountFrequency (%)
-126 12
 
< 0.1%
-125 18
 
< 0.1%
-124 18
 
< 0.1%
-123 18
 
< 0.1%
-122 18
 
< 0.1%
-121 26
< 0.1%
-120 30
< 0.1%
-119 30
< 0.1%
-118 30
< 0.1%
-117 46
< 0.1%
ValueCountFrequency (%)
313 35
 
< 0.1%
312 42
 
< 0.1%
311 42
 
< 0.1%
310 42
 
< 0.1%
309 42
 
< 0.1%
308 42
 
< 0.1%
307 217
< 0.1%
306 252
< 0.1%
305 251
< 0.1%
304 251
< 0.1%

Interactions

2025-08-14T11:48:57.688041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:18.354158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:21.639802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:25.035604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:28.198861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:31.472383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:34.781613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:38.006760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:41.118378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:44.476200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:47.706810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:50.887392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:54.154199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:57.927739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:18.663108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:21.866227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:25.277840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:28.446984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:31.855066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:35.026421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:38.237061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:41.364836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:44.705065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:47.942296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:51.122649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:54.409508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:58.177774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:18.898548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:22.109234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:25.514539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:28.695780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:32.105798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:35.281767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:38.472896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:41.609203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:44.948704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:48.184292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:51.364109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:54.871037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:58.411517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:19.127878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:22.344781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:25.747865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:28.941302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:32.340837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:35.519158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:38.704923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:41.849302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:45.216934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:48.419109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:51.600603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:55.112879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:58.689568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:19.398356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:22.597902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:26.000016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:29.186828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:32.586386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:35.779031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:38.953156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:42.270148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:45.521337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:48.670171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:51.859836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:55.386167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:58.943024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:19.673745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:22.845113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:26.251966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:29.443119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:32.828643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:36.026515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:39.197006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:42.512632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:45.789853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:48.910166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:52.104085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:55.647298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:59.195060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:19.953055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:23.092210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:26.501165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:29.705452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:33.072873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:36.279097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:39.438750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:42.770509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:46.035925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:49.158205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:52.448999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:55.919342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:59.444905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:20.216047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:23.329587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:26.753214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:29.962155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:33.328184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:36.525353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:39.675165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:43.005143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:46.274155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:49.394321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:52.705123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:56.171898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:59.700960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:20.471282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:23.848916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:27.000354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:30.210665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:33.576321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:36.776182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:39.916692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:43.254430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:46.508347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:49.650910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:52.960988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:56.431602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:59.961747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:20.704168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:24.085217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:27.245055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:30.458743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:33.813334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:37.021103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:40.151993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:43.496225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:46.745908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:49.920164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:53.193073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:56.679398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:49:00.212651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:20.937339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:24.325700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:27.487342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:30.712526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:34.054733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:37.271485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:40.390742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:43.744265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:46.987398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:50.156136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:53.422968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:56.938281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:49:00.458780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:21.163674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:24.558821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:27.728290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:30.963135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:34.292434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:37.513839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:40.627978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:43.988091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:47.220451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:50.402817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:53.661083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:57.184124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:49:00.707988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:21.405603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:24.795556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:27.973016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:31.212257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:34.544286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:37.763224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:40.873337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:44.236683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:47.467830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:50.648780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:53.910787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T11:48:57.441453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-14T11:49:15.557410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
assortmentcompetition_distancecompetition_open_since_monthcompetition_open_since_yearcompetition_time_monthdayday_of_weekis_promomonthpromopromo2promo2_since_weekpromo2_since_yearpromo_time_weeksalesschool_holidaystate_holidaystorestore_typeweek_of_yearyear
assortment1.0000.0660.0610.0800.0700.0000.1490.0050.0060.0130.0160.0920.1110.0980.0940.0040.0680.1150.5380.0060.007
competition_distance0.0661.000-0.0240.008-0.007-0.000-0.0000.071-0.0000.0030.162-0.0140.030-0.031-0.0380.0030.010-0.0450.046-0.0000.003
competition_open_since_month0.061-0.0241.000-0.2350.151-0.001-0.0060.1020.3140.0130.1290.1070.017-0.029-0.0030.1470.015-0.0340.0710.3130.082
competition_open_since_year0.0800.008-0.2351.000-0.9300.0010.0010.032-0.0350.0000.0540.0170.0310.0450.0400.0010.0020.0020.055-0.0340.005
competition_time_month0.070-0.0070.151-0.9301.0000.009-0.0010.0320.0140.0010.050-0.0240.0610.016-0.0270.0000.001-0.0030.0520.0160.064
day0.000-0.000-0.0010.0010.0091.0000.0080.040-0.0060.3150.0000.0150.0020.013-0.0650.1400.022-0.0000.0000.0800.016
day_of_week0.149-0.000-0.0060.001-0.0010.0081.0000.018-0.0190.4140.029-0.0060.003-0.009-0.1790.2040.0260.0000.168-0.0350.007
is_promo0.0050.0710.1020.0320.0320.0400.0181.0000.2330.0050.4290.1720.3010.3940.0540.0290.0070.0380.0450.1850.033
month0.006-0.0000.314-0.0350.014-0.006-0.0190.2331.0000.0410.0290.473-0.0690.0200.0620.4110.0350.0010.0070.9960.263
promo0.0130.0030.0130.0000.0010.3150.4140.0050.0411.0000.0000.0590.0160.0130.3710.0290.0110.0000.0180.1150.024
promo20.0160.1620.1290.0540.0500.0000.0290.4290.0290.0001.0000.2800.6830.9090.1190.0080.0100.0720.1080.0280.031
promo2_since_week0.092-0.0140.1070.017-0.0240.015-0.0060.1720.4730.0590.2801.000-0.121-0.0410.0800.2080.0260.0050.0740.4740.133
promo2_since_year0.1110.0300.0170.0310.0610.0020.0030.301-0.0690.0160.683-0.1211.000-0.7930.0860.0280.0080.0080.086-0.0660.614
promo_time_week0.098-0.031-0.0290.0450.0160.013-0.0090.3940.0200.0130.909-0.041-0.7931.000-0.0670.0290.007-0.0100.0830.0230.252
sales0.094-0.038-0.0030.040-0.027-0.065-0.1790.0540.0620.3710.1190.0800.086-0.0671.0000.0380.0550.0010.1120.0600.034
school_holiday0.0040.0030.1470.0010.0000.1400.2040.0290.4110.0290.0080.2080.0280.0290.0381.0000.0320.0000.0050.3820.045
state_holiday0.0680.0100.0150.0020.0010.0220.0260.0070.0350.0110.0100.0260.0080.0070.0550.0321.0000.0070.0710.0330.004
store0.115-0.045-0.0340.002-0.003-0.0000.0000.0380.0010.0000.0720.0050.008-0.0100.0010.0000.0071.0000.0980.0010.005
store_type0.5380.0460.0710.0550.0520.0000.1680.0450.0070.0180.1080.0740.0860.0830.1120.0050.0710.0981.0000.0070.010
week_of_year0.006-0.0000.313-0.0340.0160.080-0.0350.1850.9960.1150.0280.474-0.0660.0230.0600.3820.0330.0010.0071.0000.250
year0.0070.0030.0820.0050.0640.0160.0070.0330.2630.0240.0310.1330.6140.2520.0340.0450.0040.0050.0100.2501.000

Missing values

2025-08-14T11:49:01.047793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-14T11:49:04.467028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

storeday_of_weekdatesalespromostate_holidayschool_holidaystore_typeassortmentcompetition_distancecompetition_open_since_monthcompetition_open_since_yearpromo2promo2_since_weekpromo2_since_yearis_promoyearmonthdayweek_of_yearyear_weekcompetition_sincecompetition_time_monthpromo_sincepromo_time_week
0152015-07-3152631regular_day1cbasic1270.092008031201502015731302015-302008-09-01842015-07-270
1252015-07-3160641regular_day1abasic570.0112007113201012015731302015-302007-11-01942010-03-22279
2352015-07-3183141regular_day1abasic14130.0122006114201112015731302015-302006-12-011052011-03-28226
3452015-07-31139951regular_day1cextended620.092009031201502015731302015-302009-09-01712015-07-270
4552015-07-3148221regular_day1abasic29910.042015031201502015731302015-302015-04-0142015-07-270
5652015-07-3156511regular_day1abasic310.0122013031201502015731302015-302013-12-01202015-07-270
6752015-07-31153441regular_day1aextended24000.042013031201502015731302015-302013-04-01282015-07-270
7852015-07-3184921regular_day1abasic7520.0102014031201502015731302015-302014-10-01102015-07-270
8952015-07-3185651regular_day1aextended2030.082000031201502015731302015-302000-08-011822015-07-270
91052015-07-3171851regular_day1abasic3160.092009031201502015731302015-302009-09-01712015-07-270
storeday_of_weekdatesalespromostate_holidayschool_holidaystore_typeassortmentcompetition_distancecompetition_open_since_monthcompetition_open_since_yearpromo2promo2_since_weekpromo2_since_yearis_promoyearmonthdayweek_of_yearyear_weekcompetition_sincecompetition_time_monthpromo_sincepromo_time_week
101658849422013-01-0131130public_holiday1bbasic1260.062011012013020131102013-002011-06-01192012-12-310
101660651222013-01-0126460public_holiday1bextra590.012013152013020131102013-002013-01-0102013-01-28-4
101662453022013-01-0129070public_holiday1aextended18160.012013012013020131102013-002013-01-0102012-12-310
101665656222013-01-0184980public_holiday1bextended1210.012013012013020131102013-002013-01-0102012-12-310
101677067622013-01-0138210public_holiday1bextra1410.092008012013020131102013-002008-09-01522012-12-310
101677668222013-01-0133750public_holiday1bbasic150.092006012013020131102013-002006-09-01772012-12-310
101682773322013-01-01107650public_holiday1bextra860.0101999012013020131102013-001999-10-011612012-12-310
101686376922013-01-0150350public_holiday1bextra840.0120131482012120131102013-002013-01-0102012-11-196
101704294822013-01-0144910public_holiday1bextra1430.012013012013020131102013-002013-01-0102012-12-310
1017190109722013-01-0159610public_holiday1bextra720.032002012013020131102013-002002-03-011312012-12-310